Prediction Learning And Games PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Prediction Learning And Games PDF full book. Access full book title Prediction Learning And Games.
Author | : Nicolo Cesa-Bianchi |
Publisher | : Cambridge University Press |
Total Pages | : 4 |
Release | : 2006-03-13 |
Genre | : Computers |
ISBN | : 113945482X |
Download Prediction, Learning, and Games Book in PDF, ePub and Kindle
This important text and reference for researchers and students in machine learning, game theory, statistics and information theory offers a comprehensive treatment of the problem of predicting individual sequences. Unlike standard statistical approaches to forecasting, prediction of individual sequences does not impose any probabilistic assumption on the data-generating mechanism. Yet, prediction algorithms can be constructed that work well for all possible sequences, in the sense that their performance is always nearly as good as the best forecasting strategy in a given reference class. The central theme is the model of prediction using expert advice, a general framework within which many related problems can be cast and discussed. Repeated game playing, adaptive data compression, sequential investment in the stock market, sequential pattern analysis, and several other problems are viewed as instances of the experts' framework and analyzed from a common nonstochastic standpoint that often reveals new and intriguing connections.
Author | : Christoph Molnar |
Publisher | : Lulu.com |
Total Pages | : 320 |
Release | : 2020 |
Genre | : Artificial intelligence |
ISBN | : 0244768528 |
Download Interpretable Machine Learning Book in PDF, ePub and Kindle
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Author | : Tan, Wee Hoe |
Publisher | : IGI Global |
Total Pages | : 306 |
Release | : 2018-07-13 |
Genre | : Education |
ISBN | : 1522560270 |
Download Design, Motivation, and Frameworks in Game-Based Learning Book in PDF, ePub and Kindle
Game-based learning relates to the use of games to enhance the learning experience. Educators have been using games in the classroom for years, and when tied to the curriculum, commercial games are a powerful learning tool because they are highly engaging and relatable for students. Design, Motivation, and Frameworks in Game-Based Learning is a critical scholarly resource that examines the themes of game-based learning. These themes, through a multidisciplinary perspective, juxtapose successful practices. Featuring coverage on a broad range of topics such as educational game design, gamification in education, and game content curation, this book is geared towards academicians, researchers, and students seeking current research on justifying the roles and importance of motivation in making games fun and engaging for game-based learning practice.
Author | : Micheal Lanham |
Publisher | : Packt Publishing Ltd |
Total Pages | : 420 |
Release | : 2020-01-03 |
Genre | : Computers |
ISBN | : 1839216778 |
Download Hands-On Reinforcement Learning for Games Book in PDF, ePub and Kindle
Explore reinforcement learning (RL) techniques to build cutting-edge games using Python libraries such as PyTorch, OpenAI Gym, and TensorFlow Key FeaturesGet to grips with the different reinforcement and DRL algorithms for game developmentLearn how to implement components such as artificial agents, map and level generation, and audio generationGain insights into cutting-edge RL research and understand how it is similar to artificial general researchBook Description With the increased presence of AI in the gaming industry, developers are challenged to create highly responsive and adaptive games by integrating artificial intelligence into their projects. This book is your guide to learning how various reinforcement learning techniques and algorithms play an important role in game development with Python. Starting with the basics, this book will help you build a strong foundation in reinforcement learning for game development. Each chapter will assist you in implementing different reinforcement learning techniques, such as Markov decision processes (MDPs), Q-learning, actor-critic methods, SARSA, and deterministic policy gradient algorithms, to build logical self-learning agents. Learning these techniques will enhance your game development skills and add a variety of features to improve your game agent’s productivity. As you advance, you’ll understand how deep reinforcement learning (DRL) techniques can be used to devise strategies to help agents learn from their actions and build engaging games. By the end of this book, you’ll be ready to apply reinforcement learning techniques to build a variety of projects and contribute to open source applications. What you will learnUnderstand how deep learning can be integrated into an RL agentExplore basic to advanced algorithms commonly used in game developmentBuild agents that can learn and solve problems in all types of environmentsTrain a Deep Q-Network (DQN) agent to solve the CartPole balancing problemDevelop game AI agents by understanding the mechanism behind complex AIIntegrate all the concepts learned into new projects or gaming agentsWho this book is for If you’re a game developer looking to implement AI techniques to build next-generation games from scratch, this book is for you. Machine learning and deep learning practitioners, and RL researchers who want to understand how to use self-learning agents in the game domain will also find this book useful. Knowledge of game development and Python programming experience are required.
Author | : Vineeth Balasubramanian |
Publisher | : Newnes |
Total Pages | : 323 |
Release | : 2014-04-23 |
Genre | : Computers |
ISBN | : 0124017150 |
Download Conformal Prediction for Reliable Machine Learning Book in PDF, ePub and Kindle
The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detection. As practitioners and researchers around the world apply and adapt the framework, this edited volume brings together these bodies of work, providing a springboard for further research as well as a handbook for application in real-world problems. Understand the theoretical foundations of this important framework that can provide a reliable measure of confidence with predictions in machine learning Be able to apply this framework to real-world problems in different machine learning settings, including classification, regression, and clustering Learn effective ways of adapting the framework to newer problem settings, such as active learning, model selection, or change detection
Author | : Alan D. Taylor |
Publisher | : Princeton University Press |
Total Pages | : 272 |
Release | : 1999-10-12 |
Genre | : Mathematics |
ISBN | : 9780691001203 |
Download Simple Games Book in PDF, ePub and Kindle
Introductory material receives a fresh treatment, with an emphasis on Boolean subgames and the Rudin-Keisler order as unifying concepts. Advanced material focuses on the surprisingly wide variety of properties related to the weightedness of a game."--BOOK JACKET.
Author | : Leszek Rutkowski |
Publisher | : Springer |
Total Pages | : 646 |
Release | : 2013-06-04 |
Genre | : Computers |
ISBN | : 3642386105 |
Download Artificial Intelligence and Soft Computing Book in PDF, ePub and Kindle
The two-volume set LNAI 7894 and LNCS 7895 constitutes the refereed proceedings of the 12th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2013, held in Zakopane, Poland in June 2013. The 112 revised full papers presented together with one invited paper were carefully reviewed and selected from 274 submissions. The 56 papers included in the second volume are organized in the following topical sections: evolutionary algorithms and their applications; data mining; bioinformatics and medical applications; agent systems, robotics and control; artificial intelligence in modeling and simulation; and various problems of artificial intelligence.
Author | : Peter Spirtes |
Publisher | : Springer Science & Business Media |
Total Pages | : 551 |
Release | : 2012-12-06 |
Genre | : Mathematics |
ISBN | : 1461227488 |
Download Causation, Prediction, and Search Book in PDF, ePub and Kindle
This book is intended for anyone, regardless of discipline, who is interested in the use of statistical methods to help obtain scientific explanations or to predict the outcomes of actions, experiments or policies. Much of G. Udny Yule's work illustrates a vision of statistics whose goal is to investigate when and how causal influences may be reliably inferred, and their comparative strengths estimated, from statistical samples. Yule's enterprise has been largely replaced by Ronald Fisher's conception, in which there is a fundamental cleavage between experimental and non experimental inquiry, and statistics is largely unable to aid in causal inference without randomized experimental trials. Every now and then members of the statistical community express misgivings about this turn of events, and, in our view, rightly so. Our work represents a return to something like Yule's conception of the enterprise of theoretical statistics and its potential practical benefits. If intellectual history in the 20th century had gone otherwise, there might have been a discipline to which our work belongs. As it happens, there is not. We develop material that belongs to statistics, to computer science, and to philosophy; the combination may not be entirely satisfactory for specialists in any of these subjects. We hope it is nonetheless satisfactory for its purpose.
Author | : Shai Shalev-Shwartz |
Publisher | : Cambridge University Press |
Total Pages | : 415 |
Release | : 2014-05-19 |
Genre | : Computers |
ISBN | : 1107057132 |
Download Understanding Machine Learning Book in PDF, ePub and Kindle
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Author | : Robert E. Schapire |
Publisher | : MIT Press |
Total Pages | : 544 |
Release | : 2014-01-10 |
Genre | : Computers |
ISBN | : 0262526034 |
Download Boosting Book in PDF, ePub and Kindle
An accessible introduction and essential reference for an approach to machine learning that creates highly accurate prediction rules by combining many weak and inaccurate ones. Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate “rules of thumb.” A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical. This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.